Grassland as a distinct landscape type sustains biological and cultural diversity. However, its use at an unprecedentedly high intensity has had a disastrous impact on grassland ecosystems, with degeneration occurring nationwide. Traditional management policy, drafted according to the stocking rate from livestock statistics and/or in situ assessment, seems to have played no role in alleviating this trend. A major cause is the failure of decision making to consider spatial heterogeneity in the impact of grazing on grassland. To address this issue, high temporal resolution moderate-resolution imaging spectroradiometer data were used to develop a new indicator, named the grazing intensity index (GII), for quantifying grazing intensity (GI). The new index was compared with two traditional approaches and the results demonstrated that GII could substitute for both of them to quantify GI.

Smart city is the development of digital city; as its main supporting technology, the digital city geo-spatial framework has to be upgraded to the temporal-spatial information infrastructure (TSII). First, this paper proposes the concept and basic framework of smart city and defines the concept of TSII – processes, integration, mining analysis, and share time-stamps geographic data – and the related policy, regulations and standards, technology, facilities, mechanism, and human resources. The framework has four components: the benchmark of time and space, temporal-spatial big data, the cloud service platform and the related supporting environment. Second, the temporal-spatial big data and cloud service platform are elaborated. Finally, an application of TSII constructed by the Xicheng District Planning Bureau in Beijing is introduced, which provides a useful reference for the construction of smart city.

Three-dimensional (3D) building models have been widely used in the fields of urban planning, navigation and virtual geographic environments. These models incorporate many details to address the complexities of urban environments. Level-of-detail (LOD) technology is commonly used to model progressive transmission and visualization. These detailed groups of models can be replaced by a single model using generalization. In this paper, the texture features are first introduced into the generalization process, and a self-organizing mapping (SOM)-based algorithm is used for texture classification. In addition, a new cognition-based hierarchical algorithm is proposed for model-group clustering. First, a constrained Delaunay triangulation (CDT) is constructed using the footprints of building models that are segmented by a road network, and a preliminary proximity graph is extracted from the CDT by visibility analysis. Second, the graph is further segmented by the texture–feature and landmark models. Third, a minimum support tree (MST) is created from the segmented graph, and the final groups are obtained by linear detection and discrete-model conflation. Finally, these groups are conflated using small-triangle removal while preserving the original textures. The experimental results demonstrate the effectiveness of this algorithm.